


import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					@property
					def A   (					self    ) ->      int:







								'''simple docstring'''

								torch.manual_seed(0    )
								__lowercase			       = UNetaDModel(
								    block_out_channels=(3_2, 6_4)			,							layers_per_block=2			,							sample_size=3_2			,							in_channels=3			,							out_channels=3			,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')			,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')			,							)
								return model






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.dummy_uncond_unet
								__lowercase			       = KarrasVeScheduler()

								__lowercase			       = KarrasVePipeline(unet=snake_case_			,							scheduler=snake_case_    )
								pipe.to(snake_case_    )
								pipe.set_progress_bar_config(disable=snake_case_    )

								__lowercase			       = torch.manual_seed(0    )
								__lowercase			       = pipe(num_inference_steps=2			,							generator=snake_case_			,							output_type='''numpy'''    ).images

								__lowercase			       = torch.manual_seed(0    )
								__lowercase			       = pipe(num_inference_steps=2			,							generator=snake_case_			,							output_type='''numpy'''			,							return_dict=snake_case_    )[0]

								__lowercase			       = image[0, -3:, -3:, -1]
								__lowercase			       = image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__lowercase			       = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2




@slow
@require_torch
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = '''google/ncsnpp-celebahq-256'''
								__lowercase			       = UNetaDModel.from_pretrained(snake_case_    )
								__lowercase			       = KarrasVeScheduler()

								__lowercase			       = KarrasVePipeline(unet=snake_case_			,							scheduler=snake_case_    )
								pipe.to(snake_case_    )
								pipe.set_progress_bar_config(disable=snake_case_    )

								__lowercase			       = torch.manual_seed(0    )
								__lowercase			       = pipe(num_inference_steps=2_0			,							generator=snake_case_			,							output_type='''numpy'''    ).images

								__lowercase			       = image[0, -3:, -3:, -1]
								assert image.shape == (1, 2_5_6, 2_5_6, 3)
								__lowercase			       = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


